Faults in the valleys near Ottawa could rupture and produce strong, shallow earthquakes. This magnitude 5.5 scenario visualizes the effects of such an event. It does not represent the most severe earthquake that could occur, but one that is more likely and could still cause damage.

The Denali Fault spans over 200km of the Yukon Territory, and is a significant source of seismic hazard. This magnitude 7.4 earthquake scenario, centered near small communities along the Alaska Highway, visualizes the effects of a severe earthquake that could be produced by this fault.

Prospectivity model highlights areas of Canada with the greatest potential for magmatic nickel deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

In 2015, a magnitude 4.7 earthquake occurred 60 km beneath Sidney, BC. This scenario visualizes the effects of that event if it had a magnitude of 7.1.

This is a magnitude 5.0 earthquake scenario along the Burlington Toronto Structural Zone — a fault near Toronto and its surrounding region. This fault is not known to be active but demonstrates a plausible earthquake scenario for the Toronto region.

This is a magnitude 5.0 earthquake scenario under Lake Ontario, very close to Toronto. This fault is not known to be active but demonstrates a plausible earthquake scenario for Toronto region.

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

Prospectivity model highlights areas of Canada with the greatest potential for clastic-dominated zinc deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

This cartographic dataset of areas of importance for spring herring in the Magdalen Islands was produced by Fisheries and Oceans Canada (DFO) and the Magdalen Islands ZIP Committee, as part of the work to characterize the Magdalen Islands Lagoons Marine Refuge. The initiative aimed to document fishermen's ecological knowledge of spring herring, including good fishing areas and spawning grounds around the archipelago. The data comes from interviews conducted between January and April 2024 with the most experienced fishermen in the archipelago. During the interviews, participants identified directly on digital maps the locations associated with four types of observations made throughout their careers: • Question 8 — Good fishing sites; • Question 9 — Other observations of high concentrations of the resource; • Question 13a — Spawning areas and; • Question 13b — Other observations of signs of spawning. To facilitate temporal contextualization, a four-period timeline (before 1996, 1996-2002, 2003-2006, and 2007-2021) was used. The final product is a GeoPackage (.gpkg) containing 16 vector layers composed of four layers per observation type, each corresponding to one of the defined time periods. The polygons were classified according to the number of fishermen who reported each location, making it possible to assess the degree of overlap and the relative importance of the areas over time. The data are projected in NAD83 / MTM zone 4. For more information on the methodology and data, see Burbank et al. (2025). Additional information specific to the Magdalen Islands lagoons is presented in Grégoire et al. (2026).

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7